Evidence on the use of unverifiable estimates in required goodwill
impairment
*Karthik Ramanna Harvard Business School
Ross L. Watts
MIT Sloan School of Management [email protected]
First draft: January 15, 2007 This draft: January 31, 2011
Abstract
SFAS 142 requires managers to estimate the current fair value of goodwill to determine goodwill write-offs. In promulgating the standard, the FASB predicted managers will, on average, use the fair value estimates to convey private information on future cash flows. The current fair value of goodwill is unverifiable because it depends in part on management’s future actions (including managers’ conceptualization and implementation of firm strategy). Thus, agency theory predicts managers will, on average, use the discretion in SFAS 142 consistent with private incentives. We test these hypotheses in a sample of firms with market indications of goodwill impairment. Our evidence, while consistent with some agency-theory derived predictions, does not confirm the private information hypothesis.
* We thank Paul Healy, S.P. Kothari, Richard Leftwich, Sugata Roychowdhury, Edward Riedl, Richard Sloan, Eugene Soltes, Jerold Zimmerman, the anonymous referees, and seminar participants at University of Alberta, Chinese University of Hong Kong, University of Colorado, University of Connecticut, Harvard University, University of Lancaster, Massachusetts Institute of Technology, University of New South Wales, Peking University, University of Washington, and Washington University at St. Louis for helpful comments; the division of research at Harvard Business School for research assistance; and Harvard University and Massachusetts Institute of Technology for financial support. Any errors are our responsibility.
1 1. Introduction
Accounting for acquired goodwill has been subject to considerable debate for at least the past fifty years: Zeff (2005) cites disagreements over goodwill accounting rules as among the causes for the collapse of both the Committee on Accounting Principles and the Accounting Principles Board. SFAS 142, issued by the FASB in 2001, introduced a new approach to goodwill accounting by abolishing goodwill amortization and requiring all goodwill be tested periodically for impairment using estimates of its current fair value. In issuing SFAS 142, the FASB (2001, p. 7) predicted that the standard ―will improve financial reporting because the financial statements of entities that acquire goodwill and other intangible assets will [now] better reflect the underlying economics of those assets.‖ Specifically, the FASB expected financial statements generated under the standard to provide ―users with a better understanding of the expectations about and changes in [goodwill and other intangible assets] over time.‖ That is, the board expected managers will, on average, use estimates of goodwill’s fair value to convey private information on future cash flows.
The SFAS 142 approach to goodwill accounting represents a significant innovation over prior practice and standards in that it relies solely on management estimates of goodwill’s current value.1
The current fair value of goodwill is a function of management’s future actions, including managers’ conceptualization and implementation of firm strategy. As such, it is difficult to verify and audit. In effect, we expect the subjectivity inherent in estimating goodwill’s current fair value is greater than that in most other asset classes such as accounts receivables, inventories, and plant, making the goodwill impairment test under SFAS 142 particularly unreliable. Ex post, if a fair-value estimate used to justify goodwill non-impairment is not realized, a manager can claim it was due to factors outside her control (e.g., macroeconomic conditions). It is difficult to falsify such a claim in a court of law (the claim cannot be ―objectively characterized as true or false,‖ Ollman v. Evans, 750 F.2d 970, D.C. Cir., 1984). Thus, we hypothesize managers exploit the SFAS 142 goodwill impairment test consistent with private incentives, as predicted by agency theory.
1 Prior to SFAS 142, goodwill was impaired only when certain associated long-lived assets were also impaired (SFAS 121); moreover, goodwill was also subject to periodic amortization (APB 17).
2
How managers will use the opportunity to incorporate estimates of goodwill’s fair value in practice is an empirical question. We investigate managers’ implementation of the SFAS 142 goodwill impairment test in a sample of firms with market indications of goodwill impairment. We examine whether goodwill non-impairment in the sample is associated with proxies for managers’ private information on positive future cash flows and/or with agency-based motives, including management’s interests in increasing their compensation and in shielding their reputation from the implications of a goodwill write-off. We do not find evidence to confirm the private information argument, but we find some evidence consistent with agency-based predictions. We also test whether goodwill non-impairment varies with firm characteristics predicted by Ramanna (2008) to facilitate discretion under SFAS 142. These characteristics include: (1) the number and size of a firm’s business units; and (2) the proportion of a firm’s net assets that are unverifiable. We find goodwill non-impairments increase in proxies for both characteristics.
To generate the sample of firm-years with market indications of goodwill impairment, we begin with firms that have both: (i) book goodwill; and (ii) equity-market-values greater than equity-book-values. Among these firms, we retain only those that end each of the two subsequent fiscal years with book-to-market ratios (BTM) above one (where book values are calculated before the effect of any goodwill impairment, but after the effect of any other asset write-off). The condition BTM > 1 suggests the market expects goodwill impairments; however the condition can also be generated by certain GAAP rules on contingent losses, deferred taxes, and the impairment of (non-goodwill) long-lived assets.2 To mitigate this possibility, we require sample firms to have two consecutive years of BTM > 1. Under such restriction, we argue goodwill is likely to be impaired; but our tests remain subject to the caveat that sample firms avoid write-offs because of certain GAAP rules. We investigate the determinants of goodwill non-impairment at the end of the second fiscal year with BTM > 1, conditional on the firms having non-zero goodwill balances at the beginning of that year. There are 124 firm-years on COMPUSTAT that meet the sample selection criteria between the years 2003 and
2 In particular, among certain (non-goodwill) long-lived assets, impairment is triggered only when the undiscounted sum of future cash flows attributed to an asset is less than that asset’s book value.
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2006, our sample period.3 The frequency of goodwill non-impairment in sample firm-years (i.e., the second fiscal year with BTM > 1) is 69%.
It is possible that managers of sample firms avoid goodwill write-offs because they have (or believe they have) private information on positive future cash flows. We identify firms likely to have favorable private information as those firms with either positive net share-repurchase activity or positive net insider buying. Both activities suggest management believes the firm is undervalued. We examine whether sample firms without goodwill write-offs are more likely (than those with the write-offs) to have positive repurchase or insider buying activity. If this is the case, the data support the argument that managers’ private information drives non-impairments. We find the frequency of firms with positive net share-repurchase activity among non-impairers (24%) is statistically indistinguishable from that among impairers (24%). Further, the frequency of firms with positive net insider buying among non-impairers (22%) is also statistically indistinguishable from that among impairers (18%).
Our use of share-repurchase and insider buying activity as indicators of managers’ private information is subject to an important caveat. Such activities, if interpreted by the market as being caused by managers exploiting favorable private information, can result in increased stock returns such that a firm’s BTM is no longer above 1, thus excluding the firm from our sample. Accordingly, our tests using these proxies can be of low power. To mitigate this concern, we conduct two additional tests of the private information hypothesis: the first using two proxies for positive private information based on linguistic analyses of sample firms’ 10-Ks; the second based on an ex-post analysis of sample firms’ stock returns.4
Our first linguistic proxy for positive private information, Achievement, is an indicator set to one when the proportion of ―achievement‖-related words in a sample firm’s 10-K is greater than that in an industry-size match. ―Achievement‖-related words are defined in Appendix B and are constructed from a similarly named category in the
3 Our sample begins in 2003 because that was the first full-year of SFAS 142 adoption after the standard’s transition adoption year; our sample ends in 2006 because financial data beyond 2006 was not available when we initiated this study.
4 To further address this concern, we also investigate whether firms with share repurchases and insider buying are disproportionately more likely to be eliminated from our sample due to our requirement that sample firms end the fiscal year with BTM > 1. We do not find evidence that this is the case.
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Linguistic Inquiry & Word Count (LIWC) dictionary (Pennebaker, Francis, and Booth, 2001).5 High Achievement is expected to signify good news, which in turn can justify the non-impairment of goodwill. We find that the proportion of firms with high Achievement among non-impairers in the sample (57%) is statistically indistinguishable from that among sample impairers (54%). Thus, positive private information as manifested by an incidence of Achievement is unable to explain non-impairment in the sample.
Li (2008) introduces to the accounting literature the use of the Fog Index to analyze 10-Ks: this index is our second linguistic proxy for positive private information. A Fog Index over 18 denotes unreadability and can measure attempts at obfuscation in the annual report. A priori, a firm with positive private information is less likely to engage in obfuscation, and thus less likely to generate a 10-K with a Fog Index over 18. If positive private information motivates non-impairment, we expect a lower prevalence of 10-Ks with Fog Indices over 18 among sample non-impairers than among sample impairers. However, we find the proportion of non-impairing sample firms with Fog Indices over 18 (78%) is statistically higher than that of impairing sample firms (59%).6 Thus, non-impairing sample firms appear to be more likely to obfuscate in 10-Ks, inconsistent with favorable private information motivating non-impairment in the sample. Inferences based on the linguistic proxies are subject to a ―cheap talk‖ caveat; it is possible that the use of words to signify ―achievement‖ or to obfuscate is costless, making the proxies noisy. Accordingly, our final tests of the private information hypothesis are based on an analysis of sample firms’ one-year-ahead stock returns. The purpose is to determine whether, on average, non-impairers are more likely to have higher one-year-ahead stock returns than impairers. If this is the case, then non-impairment is consistent with managers having positive private information, information that subsequently (over one year) becomes public. For the 124 firms in our sample, stock returns data over the following 12 months continue to be available on CRSP for 96 firms. Sixty-four of these 96 firms are non-impairers in the sample year; the other 32 are impairers. The mean and median one-year-ahead stock returns across non-impairers and
5 LIWC has been used previously in computational linguistic analyses across the social and behavioral sciences (see Tauzczik and Pennebaker, 2009, for a review), including accounting research (e.g., Li, 2008). 6 Throughout the paper, statistical significance is inferred at the two-tail 90% confidence level or higher.
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impairers are not statistically distinguishable (non-impairers’ mean=22.9%, median=12.4%; impairers’ mean=17.2%, median=13.3%).7
To investigate whether non-impairment is associated with motives predicted by agency theory to affect management’s accounting choice, we test for the cross-sectional variation in goodwill write-offs with proxies for CEO compensation concerns, CEO reputation concerns, asset-pricing concerns, exchange-delisting concerns, and concerns relating to debt covenant violation. Beatty and Weber (2006) predict from prior literature that goodwill write-offs in the initial adoption year of SFAS 142 vary in these motives; they find evidence consistent with some of their predictions. Our agency-based predictions, while derived from those in Beatty and Weber, differ where appropriate, to account for differences in incentives between the transition year and subsequent years. We find no evidence to confirm that asset-pricing concerns and exchange-delisting concerns are associated with goodwill write-off decisions. The result on asset-pricing concerns is consistent with firms’ stock prices already reflecting goodwill as impaired, a condition on which we attempted to select the sample. In contrast, we find a statistically higher proportion of firms with covenants that are likely to be goodwill inclusive among sample impairers (78%) than among sample impairers (63%). The proportion of non-impairing sample firms whose CEOs are likely to have goodwill-inclusive compensation contracts (57%) is also statistically higher than that of impairing sample firms (39%). In multivariate tests that control for firm-level economics and management’s private information, there is some further evidence that debt-covenant and CEO compensation incentives are associated with non-impairment.
Also in the multivariate tests, we find evidence that non-impairment increases in CEO tenure. Long-tenured CEOs are more likely to have initiated the mergers that generated the goodwill now indicated by the market as impaired. Thus, if non-impairment is motivated by managers’ interests in shielding their reputation from the implications of
7
We use data from Thomson One Banker to follow up on the status of the remaining 28 sample firms (i.e., 124 minus 96) that are dropped from the CRSP database in the post-sample year. A detailed analysis of these firms is available in Section 3. To summarize, most of these firms are acquired, delisted, or thinly traded; statistical comparisons within this subsample are precluded by the low n.
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a goodwill write-off, long-tenured CEOs are less likely to authorize goodwill write-offs.8 In the multivariate tests, none of the proxies for managers’ positive private information described earlier are statistically associated with non-impairment.
The evidence in this paper is consistent with managers avoiding timely goodwill write-offs under SFAS 142 in circumstances where they have agency-based motives to do so, despite market indications that such write-offs are due. The unverifiable nature of fair-value estimates of goodwill makes such behavior predictable under agency theory (Watts, 2003; Ramanna, 2008). The evidence does not confirm the FASB’s implicit assumption that managers will use estimates of goodwill’s fair value to convey private information on future cash flows. At a minimum, the results suggest, on average in our sample, SFAS 142 is generating financial reports that do not reflect economic reality with respect to goodwill. Even if contracts completely adjust for this deficiency (which is unlikely), the standard imposes costs in the sample: in particular, compliance costs and the costs of managers continuing negative NPV operations in order to avoid write-offs.
Our evidence is consistent with prior results on untimeliness of asset write-downs (e.g. Elliott and Shaw, 1988) and on the role of managerial incentives in write-down decisions (Francis, Hanna, and Vincent, 1996). Beatty and Weber (2006) also study the role of agency-based incentives in the implementation of SFAS 142, but in its adoption year; we extend their study to the four subsequent years, and moreover, attempt to determine whether managers’ private information can explain impairment decisions. Given the innovative nature of SFAS 142 goodwill accounting rules, our evidence provides new insights into whether these rules are effective. In particular, since goodwill is no longer amortized, write-offs are the only way managers are held accountable in the income statement for unallocated acquisition premiums. If, as we find, goodwill write-offs are on average motivated by managers’ private incentives, SFAS 142 is generating little accountability for acquired goodwill. This finding is consistent with the observation that both practice prior to the existence of regulated standard setting and standards prior to SFAS 142 did not rely solely on fair-value-based impairment testing for goodwill accounting. More generally, our evidence can speak to the use of unverifiable discretion
8 Avoiding timely impairments to prevent debt covenant violations can also be motivated by CEO reputation concerns (in addition to transferring wealth from debtholders to shareholders) since failure to do so can be perceived as managerial incompetence.
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in financial reporting. Valuing goodwill is part of a broader shift in standard-setting towards valuing the firm (i.e., fair value). Our evidence suggests that fair values, when extended to assets with unauditable valuations, are likely to compromise financial reporting’s role as a management control system.
The remainder of the paper is organized as follows. Section 2 describes the goodwill impairment test in SFAS 142, the construction of the sample and variables, and the research design. Descriptive statistics, the results of univariate tests, and the results of construct validity tests on a sample of firms under SFAS 121 are presented in Section 3. That section also presents the results of multivariate tests and some additional analyses. Section 4 concludes with a summary and implications.
2. The study
In this section, we first explain (in §2.1.) the SFAS 142 goodwill impairment rules. In §2.2., we describe the sample selection procedure and address potential caveats in the process. In §2.3., we discuss the possible motives (and our proxies for those motives) for managers to avoid impairment write-offs. In §2.4., we describe two firm characteristics (and their empirical proxies) that can provide managers with the ability to use the unverifiable discretion in SFAS 142. §2.5., describes the research design for multivariate tests.
2.1. Unverifiable discretion in the SFAS 142 goodwill impairment test
Prior to SFAS 142, accounting for acquired goodwill was governed by APB 17 (AICPA, 1970) and SFAS 121 (FASB, 1995). Under these standards, firms had the option to account for acquisitions using the pooling-of-interests method, thereby avoiding goodwill recognition altogether. Goodwill, when recognized, was subject to periodic amortization. Goodwill was also subject to impairment, but only when certain associated long-lived assets were also impaired. The test for asset impairment was based on comparing undiscounted future cash flows to book values. SFAS 142 abolished goodwill amortization and required instead an impairment-only approach to goodwill. Further, SFAS 142 no longer tied the goodwill impairment decision to impairment decisions on
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related long-lived assets. Instead, goodwill is now impaired based on a comparison of a fair-value estimate of goodwill with the book value of goodwill.
SFAS 142 lays out the following procedure for goodwill impairment. All acquired goodwill is initially allocated among the ―reporting units‖ of a firm. Generally, a reporting unit is an operating segment, or a component thereof, if that component constitutes a business with discrete financial information that is regularly reviewed by management (SFAS 142, §30). Goodwill is tested for impairment at this reporting unit level. For a given reporting unit, the goodwill impairment test is a two-step procedure as described below.
1. The reporting unit’s total fair value is estimated by management (or their agents). This fair value is then compared to the unit’s total book value. If the fair value is greater than the book value, Step 2 is skipped and no impairment loss is recognized.
2. If the unit’s estimated fair value is less than its book value, the fair value of the unit’s goodwill is estimated. The fair value of goodwill is defined as the difference between the unit’s total fair value (from Step 1) and the sum of the fair values of the unit’s non-goodwill net assets. The fair value of goodwill is then compared to the book value of goodwill. Any excess of goodwill’s book value over its fair value is recorded as the unit’s impairment loss (no loss or gain is recognized if the goodwill’s fair value estimate exceeds its book value). Goodwill impairment losses from the firm’s various reporting units are aggregated and reported as a separate above-the-line item in the income statement.
There are three layers of discretion in the impairment procedure described above. First, acquired goodwill, which represents rents expected from an acquisition, must be allocated across reporting units; second, the discounted future value of those reporting units must be estimated; and third, the current value of the units’ net assets (including non-goodwill intangibles) must be estimated. The discretion in the first two layers is difficult to audit in that it is ex post unverifiable. A similar argument can be made about
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the discretion in the third layer, particularly with regards to current-value estimates of thinly traded assets and liabilities.
While there is discretion associated with estimating accruals in nearly all areas of accounting, we argue the subjectivity inherent in estimating the current fair value of goodwill is greater than that in most other asset classes such as accounts receivables, inventories, and plant. There are two bases for this argument. (1) Uncertainty in future cash flows: The uncertainty in future cash flows associated with capitalized goodwill is higher (relative to other capitalized assets) because goodwill represents a present-value estimate of future rents. The realization of those rents depends on several unpredictable factors such as the firm’s competitive position vis-à-vis its customers, its suppliers, its employees, and the regulatory environment. (2) Moral hazard: The ability to realize the value embedded in book goodwill is contingent on management’s future effort. Allowing managers to fair-value goodwill can result in managers receiving compensation (by overstating profits through non- impairment) on projects that are likely to fail. When that failure does occur, managers cannot be held accountable because it is impossible to objectively assess whether the failure was due to management actions.9
2.2. Sample selection
Our objective is to test whether firms with the ability and motives to manage SFAS 142 goodwill impairment losses actually do so. To do this, we need to identify a sample of firm-years where goodwill is likely impaired. We use the presence of book goodwill and the time-series of firms’ book-to-market ratios (BTM) to select our sample (where, as noted earlier, BTM is calculated after the effect of all non-goodwill write-offs, but before the effect of any goodwill impairment). We begin with firms that have equity-book-values < equity-market-values and at least $1 million of book goodwill at the end of year t-2. Then, we retain only those firms that end year t-1 with BTM > 1. When a firm goes from having equity-book-values < equity-market-values (in year t-2) to having BTM > 1 (in year t-1), there is likely an overstatement in its book value, suggesting a write-off
9 Unlike moral hazard implicit in valuing items like net accounts receivable (where subsequent management effort on collecting receivables is observable and can be entered into evidence), it is very difficult to make the case (in the event of ex post litigation) that goodwill losses were due to management inaction (rather than macroeconomic conditions). That is, the claim cannot be ―objectively characterized as true or false‖ (Ollman v. Evans, 750 F.2d 970, D.C. Cir., 1984), the legal standard for verifiability.
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is due. However, such a change can be associated with no write-off if the decline in market value is attributable to circumstances where GAAP does not require recognizing a contemporaneous expense (e.g., certain contingencies, deferred taxes, pensions, etc.).10 Further, GAAP rules on the impairment of certain (non-goodwill) long-lived assets can also result in firms with BTM > 1 not taking write-offs (under these rules, impairment is triggered only when the undiscounted sum of future cash flows attributed to an asset is less than that asset’s book value).
To minimize the circumstances where the change from equity-book-values < equity-market-values (in year t-2) to BTM > 1 (in year t-1) is not associated with GAAP requirements for a contemporaneous write-off, we limit our sample to firms where BTM stays above one for an additional fiscal year (year t). Additionally, we require that these firms begin year t with a non-zero goodwill balance. We argue that firms with two successive years of BTM > 1 are likely to have impairment in net assets. Further, since the firms have goodwill on their books, we expect at least some of that write-off to be in goodwill. Note if a firm takes an adequate write-off from an account other than goodwill, it is, by selection, not in our sample because its BTM should no longer be greater than one. We intentionally exclude negative book-value firms and firms with BTM < 1 from our analysis since the case for impairment in their goodwill is less compelling.
We examine the determinants of goodwill impairment in the second fiscal year with BTM > 1 (i.e., in year t). There are 124 ―year t‖ observations in the COMPUSTAT database that meet our sample criteria. The observations are from years 2003 through 2006. SFAS 142 was promulgated in June 2001 and mandatory adoption was required for fiscal years beginning after December 15, 2001. We exclude the initial adoption year (2002) from our year t analysis of impairments because in 2002 firms were permitted to ascribe goodwill impairments below-the-line to a ―change in accounting principle;‖ in all subsequent years, impairments are charged above-the-line, to ―income from continuing operations.‖ Beatty and Weber (2006) find evidence that absent contracting incentives, firms accelerate impairments into the adoption year to qualify for below-the-line
10 Even in such circumstances, assuming the decline in market value is permanent, auditors are likely to want managers to take timely write-offs.
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accounting treatment. Thus, the factors that facilitate adoption-year impairments likely differ from the factors that facilitate impairments in subsequent years.11
One can argue that the sample selection procedure is biased towards identifying firms that are not representative of the general population. Firms with two consecutive years of BTM>1 are uncompetitive or are otherwise suffering from serious economic woes that make accounting compliance issues secondary. The incidence and properties of non-compliance in our sample are therefore not representative of accounting practice in the general population. An analogy to this argument is a criticism of a study that documents properties of brake failures in cars that travel over 50 mph in a 30 mph zone on the basis that most drivers do not engage in such activity. Cars likely do much more damage when their brakes fail at over 50 mph, so failures at this speed are likely to represent a large share of the costs of brake failure and thus be of interest to stewards of automobile safety. Similarly, we argue evidence on the properties of SFAS 142 non-compliance among firms with two consecutive years of BTM > 1 is likely to be of interest to managers, investors, and regulators. SFAS 142 is an impairment standard and our sample represents firms whose goodwill is very likely impaired. We argue this is where an effective impairment standard should work.
2.3. Motives to manage goodwill impairment losses
2.3.1. Managers’ private information on positive future cash flows
Standard setters imply that the goodwill impairment test in SFAS 142 allows managers, on average, to convey private information on future cash flows. In explaining how SFAS 142 improves financial reporting, the FASB (2001, p. 7) argues the standard provides ―users with a better understanding of the expectations about and changes in [goodwill and other intangible assets] over time.‖ If this is the case, managers’ failure to impair goodwill can be attributed to information asymmetries between managers and shareholders, in particular, situations in which managers have favorable private information on future cash flows. We identify firms whose managers are likely to have positive private information as those with either positive net share-repurchase activity or
11 Firms that accelerated impairments into the adoption year to create write-off loss reserves are unlikely to be in our sample since we filter out firms without positive goodwill balances.
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positive net insider buying as of the end of year t (i.e., the second full fiscal-year with BTM>1). Both activities can be interpreted as rational responses by managers to situations where their stock is undervalued. We code firms with positive net share-repurchase activity using the indicator variable Repurchase; firms with positive net insider buying using the indicator variable Inside; and firms with either of these two activities using the indicator variable InfoAsym. In our tests, we examine the univariate association of non-impairment in our sample with InfoAsym, Repurchase, and Inside, and examine in multivariate regressions the cross-sectional variation in impairment decisions with InfoAsym.
InfoAsym, Repurchase, and Inside are likely to be effective measures of managers’ positive private information to the extent that managers are not so cash constrained that they cannot engage in share buying activity. Moreover, such share buying activity, if interpreted by the market as being caused by managers exploiting favorable private information, can result in increased stock returns such that a firm’s BTM is no longer above 1. In this case, the firm will not be in our sample, and our tests using InfoAsym and associated proxies can be of low power.12 To generate a measure of managers’ positive private information that is not limited by these concerns, we examine the extent to which managers discuss their ―achievements‖ in the year t form 10K filing. More ―achievements‖ can signify good news, which in turn can justify the non-impairment of goodwill. The Linguistic Inquiry & Word Count (LIWC) software (Pennebaker et al., 2001) allows researchers to assess the proportion of words in a document that are associated with ―achievement.‖ LIWC has been used previously in computational linguistic analyses across the social and behavioral sciences (see Tauzczik and Pennebaker, 2009, for a review), including accounting research (e.g., Li, 2008). LIWC contains an internal dictionary of English words related to achievement (among
12 To investigate this concern further, we look at firms eliminated by our sample selection procedure for evidence that this group has a disproportionately higher incidence of positive private information (i.e.,
InfoAsym=1). In particular, we analyze all firms with year ―t‖ in the 2003–2006 period, and with BTMt-2 <
1 and BTMt-1 > 1. There are 366 such firms (of these, 124 firms also have BTMt > 1; these are the firms in
our primary sample). We are interested in determining if among the 366 firms, those with BTMt > 1 (124
firms) have a lower frequency of InfoAsym=1 than those with BTMt < 1 (242 firms). The frequency of
firms with InfoAsym=1 among the BTMt > 1 sample is 41.1% (51 of 124 firms), which is actually higher
than that among the BTMt < 1 sample, 28.5% (69 of 242 firms); the comparison is statistically significant
(chi-square p-value is 0.015). The result is inconsistent with the concern that our sample selection procedure disproportionately excludes firms with positive private information.
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other categories): the dictionary has been validated by having independent evaluators score hundreds of test documents, comparing their results to those of the LIWC software (Pennebaker and King, 1999). Because the ―achievement‖ dictionary in LIWC also contains words that are antonyms to achievement (e.g., ―beaten,‖ ―defeated,‖ ―unsuccessful‖), for all subsequent tests we edit the LIWC dictionary to exclude these antonyms.13 The ―achievement’ dictionary used in our tests is reproduced in Appendix B. To construct our measure of achievement (hereafter denoted, Achievement), we define a dummy variable set to one if the proportion of ―achievement‖-related words (as defined in Appendix B) in the sample firm’s year t 10K is greater than the corresponding proportion in its size-match. The size-match is a firm in the same 3-digit NAICS industry with the closest year t-1 sales. We use a size-match in defining Achievement because the raw proportion of ―achievement‖-related words as output by LIWC is by itself difficult to objectively assess.
Achievement can be a noisy measure for private information if use of words connoting ―achievement‖ in the 10-K is costless, i.e., if Achievement is ―cheap talk.‖ Accordingly, we develop an additional linguistics-based proxy for managers’ positive private information: the proxy is denoted Fog. Li (2008) introduces to the accounting literature the use of the Fog Index to analyze 10-Ks. A Fog Index over 18 denotes unreadability and can measure attempts at obfuscation in the annual report. We define the variable Fog as a dummy set to one if the Fog Index of a sample firm-year’s 10-K is greater than 18. A priori, a firm with positive private information is less likely to engage in obfuscation and generate an unreadable 10-K, i.e., a value of Fog=1. Thus, if positive private information motivates non-impairment, we expect a lower prevalence of Fog=1 among sample non-impairers than among sample impairers.
If a legitimate justification of non-impairment requires complex language that the Fog Index captures as ―obfuscation,‖ our tests based on Fog are confounded. Accordingly, in a final series of tests of the private information hypothesis, we study sample firms’ one-year-ahead stock returns. In particular, we investigate whether sample non-impairers are more likely to have, on average, higher one-year-ahead stock returns than sample impairers. If this is the case, then non-impairment is consistent with
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managers having positive private information that is revealed publicly over the following one year. We report on the results of tests based on proxies described herein in Section 3.
2.3.2. Incentives predicted by agency theory
Agency theory predicts managers (all else equal) will on average use unverifiability in accounting judgment, such as that in SFAS 142 impairment tests, to opportunistically manage financial reports (Watts, 2003; Ramanna, 2008). Beatty and Weber (2006) study firms’ agency-based motives to delay goodwill losses in the SFAS 142 transition period. They argue from prior literature that the decision to delay goodwill losses is based on: debt and compensation contracts written on goodwill accounts (Watts and Zimmerman, 1986), management reputation (Francis et al., 1996), and equity-asset-pricing concerns (i.e., the responsiveness of stock prices to goodwill-inclusive earnings, Fields, Lys, and Vincent, 2001). Beatty and Weber also hypothesize that exchange delisting concerns can affect the impairment loss recognition decision when delisting is triggered by goodwill-inclusive covenants. In empirical tests, they find support for all motives except equity-asset-pricing concerns. Our tests on the variation in goodwill impairment across agency-based motives are based on predictions in Beatty and Weber, with some important modifications to account for differences in incentives between the transition year and subsequent years, as noted below.
1. Contracting motives:
(a) The costs of violating debt covenants (CovDebt): Our proxy for the cost of violating debt covenants is the product of the ratio of current period debt to prior period assets and an indicator if the firm has an outstanding net worth or net income based debt covenant. Net worth and net income based covenants are goodwill inclusive; thus, the existence of such covenants in a firm is likely to incent management decisions on goodwill write-offs. For firms with debt contracts written on accounting numbers, violating a covenant will be more costly, the greater its leverage. Thus, we multiply the covenant indicator variable by leverage.14 Beatty and Weber measure the debt-covenant incentives on
14 Dichev and Skinner (2002) find that leverage is a relatively noisy proxy for the probability of debt covenant violation; however, holding constant this probability, leverage is likely a good proxy for the cost
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off decisions using a firm-specific covenant slack variable that attempts to capture the significance of a firm’s goodwill account balance to meeting its net worth covenant requirements. We cannot use a similar variable because numerical data on the net worth covenant requirements for our sample are not available (Beatty and Weber’s sample is selected in part on the availability of these data; our sample is selected on market expectations of a goodwill write-off, resulting in smaller firms that are unlikely to disclose detailed covenant data). Our proxy on debt-covenant incentives is admittedly weaker than that in Beatty and Weber, potentially reducing the power of our tests.
(b) Managers’ accounting-based compensation (Bonus): Our proxy for managers’ accounting-based compensation concerns is an indicator for whether a firm’s CEO received a cash bonus during the year in question. Murphy (1999) reports that accounting-based compensation is usually paid out as a cash bonus and that accounting-based compensation contracts are usually written on net income (and so include the effect of goodwill write-offs). Thus, we expect sample firms with Bonus=1 to be less likely to take goodwill write-offs. In the case of compensation incentives as well, our prediction and proxy differ from that in Beatty and Weber. In their study, Beatty and Weber examine how the decision to accelerate goodwill write-offs to the SFAS 142 transition year depends on the presence of management compensation contracts that exclude special items (transition year goodwill write-offs are classified as a special item). Since we are interested in the role of management compensation incentives in write-offs on an ongoing basis, the inclusion or exclusion of special items in compensation contracts is irrelevant to us.
(c) The firm being traded on an exchange with accounting-based delisting requirements (Delist): Beatty and Weber report that firms listed on the NASDAQ and AMEX are subject to goodwill-inclusive accounting-based delisting requirements. OTC listed firms do not have such delisting requirements, while NYSE listed firms face delisting based on subjective criteria. To capture exchange
of debt covenant violation (the more debt a firm has, the more costly it will be to renegotiate contracts once covenants are violated). Further, we expect the probability of covenant violation in our sample (firms with two years of BTM > 1) is relatively high.
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delisting concerns, we create a dummy variable, Delist, set to one if the firm trades on NASDAQ or AMEX; zero otherwise. This variable is similar to that in the Beatty and Weber study.
2. Reputation motives, managers’ interests in shielding their reputations from the implications of a goodwill write-off (Tenure): Beatty and Weber argue that among firms with book goodwill, CEOs with longer tenures are more likely to have been involved in the acquisitions that generated that goodwill. To avoid reputation costs, such long-tenured CEOs are less likely to take goodwill write-offs. In our study, as in Beatty and Weber, CEO tenure is measured as the number of years the incumbent CEO has held that office.
3. Valuation motives, equity-asset-pricing concerns (AsstPrc): We use the earnings response coefficient (ERC) to measure the capitalization of earnings in returns. If equity-asset-pricing concerns affect managers’ accounting decisions (e.g., Fields et al., 2001), including their impairment decisions, non-impairment is likely to increase in ERC. Following Beatty and Weber, we define ERC for a given firm-year as the coefficient from a regression of the firm’s share price on its operating income using at least 16 and up to 20 quarters of data prior to the firm-year. Importantly, in our setting, in contrast to that in Beatty and Weber, the prediction on AsstPrc’s impact on write-off decisions is more ambiguous. This is because we attempt to select our sample on market indications of impaired goodwill; if we are successful in this regard, our sample firms’ stock prices will already reflect goodwill as impaired, mitigating the incentives generated by asset-pricing concerns.
2.4. Financial characteristics that facilitate unverifiable discretion under SFAS 142 In the prior section, we discussed some of the potential motives for impairment management. In addition to having the motives to manage impairment losses, firms must have the ability to do so. In this section, we discuss how the rules in SFAS 142 can make it easier for firms with certain financial characteristics to manage impairment losses. From Ramanna (2008), below we identify two firm financial characteristics that increase
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firms’ unverifiable discretion to determine impairment under SFAS 142: the number and size of reporting units; and the unverifiable net assets in reporting units.
2.4.1. Number and size of reporting units
When a firm recognizes goodwill in an acquisition, SFAS 142 requires the firm allocate that goodwill among the reporting units that benefit from the acquisition. If the rents that goodwill represents are generated jointly by the units, any allocation is arbitrary and there is no way to meaningfully allocate goodwill: any one allocation scheme is as good as another (Watts 2003; Roychowdhury and Watts, 2007). For a given firm, the larger the number of reporting units and the larger the size of those units relative to acquired goodwill, the greater the flexibility in allocating goodwill. This initial flexibility in goodwill allocation provides the opportunity to later avoid or overstate impairment losses. Goodwill can be allocated to units where subsequent impairment can be masked by the units’ internally generated unrecognized gains or losses. Managers can allocate goodwill either to low growth units to accelerate impairment (a big bath), or to high growth units (with existing unrecorded internally generated growth options) to delay impairment. The larger and more numerous the reporting units, the greater is management’s flexibility in determining future impairment losses.15
Empirical Proxies for Number and Size of Reporting Units
Ln(Seg): Empirical data on ―the number and size of reporting units‖ are not readily available. SFAS 131, however, requires firms to disclose data on their business segments. We use the number of business segments as our proxy for the number and size of reporting units. Reporting units are at least as numerous as business segments (SFAS 142, §30). Thus, using business segments as proxies understates the number of reporting units. This, in turn, biases against finding an association between impairment delays and the number of reporting units.
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Once goodwill is allocated among reporting units, reallocation in future years is not permitted (SFAS 142, §34). Thus, in principle, the ―number and size of reporting units‖ provides flexibility only at the time of acquisition. However, firms can reorganize their reporting structures in future years (SFAS 142, §36), effectively leading to a reallocation in acquired goodwill.
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HHI: We also use a variant of the Herfindahl-Hirschman Index (HHI) as a proxy for the number and size of reporting units. We calculate each firm’s HHI as follows.
n i i s HHI 1 2 ) ( ;
where n is the number of business segments in the firm and si is the ratio of the ith business-segments’ sales to total firm sales. Thus, HHI is an index of segment concentration within a firm. HHI ranges from zero to one. If a firm has only one segment, then its HHI is one; if a firm has several segments, but one of them is much larger than the others, its HHI is close to one. As the number of segments increases, and as segments become of similar size, the firm’s HHI gets closer to zero. Thus, an HHI close to zero indicates a firm with several equally sized segments, while an HHI close to one indicates a firm with a few disproportionately sized segments. In using HHI to proxy for the number and size of reporting units, note that low HHI (several equally sized segments) offers the greater flexibility associated with more and larger reporting units, while high HHI (few disproportionately sized segments) offers the lesser flexibility associated with fewer and smaller reporting units. Thus, HHI is expected to be negatively associated with Ln(Seg).
2.4.2. Unverifiable net assets in reporting units
If a reporting unit fails Step 1 of the impairment test (i.e., if the unit’s fair value to book value ratio is less than one), management must estimate the fair value of the unit’s goodwill under Step 2. That estimate is calculated as the difference between the unit’s total fair value (from Step 1) and the fair value of the unit’s constituent net assets (excluding book goodwill). Thus, in Step 2, managers must obtain fair-value appraisals for all of the unit’s assets and liabilities. For units that have a larger proportion of net assets (excluding goodwill) without readily observable market values (hereafter, unverifiable net assets), assessing fair values of net assets introduces additional subjectivity in determining impairment losses. Subjectivity in appraising the fair values of net assets other than goodwill results in subjectivity in estimating the fair value of goodwill, and consequently in estimating the amount of impairment loss. The subjectivity
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suggests that units with more unverifiable net assets have greater ability to manage goodwill impairment losses.
Empirical Proxies for Unverifiable Net Assets in Reporting Units
UNA: We compute the ratio of [Cash + All Investments and Advances – Debt – Preferred Equity] to [Assets – Liabilities]. The denominator in this ratio is total net assets, while the numerator is intended to proxy for that component of net assets whose fair-values are likely most verifiable (Richardson, Sloan, Soliman, and Tuna, 2005). Thus, this ratio is intended to capture the verifiability of net assets (VNA). Items excluded from the numerator include plant and equipment, receivables, payables, inventories, advances, etc. Fair-value estimates of these items are likely less verifiable than cash, investments, debt, and preferred equity. Thus, as the VNA ratio decreases, subjectivity in estimating the fair value of goodwill is expected to increase. To obtain a measure that increases in the subjectivity of estimating the fair value of goodwill, we multiply VNA by -1. We then rank the resulting value in-sample and denote it UNA, where ―UNA‖ refers to the unverifiability of net assets.16
IndLev: A potential problem with UNA is that it homogenizes the net assets considered unverifiable across all industries. Fabricant (1936) reports that in a sample of 208 large listed industrial US firms for the period 1925-1934, property, plant, and equipment write-ups were more numerous (70) than investment write-ups (43). Watts (2006, p. 54) argues the property, plant, and equipment written up were likely to be general, non-firm-specific assets for which market prices were more observable. If that is true, UNA measures unverifiability with error. Consequently, as an alternate proxy for the unverifiability of net assets, we also use the firm’s industry-average debt-to-assets.
Leverage can be a good proxy for non-firm-specific assets (Myers, 1977; Smith and Watts, 1992). Such assets are more likely to have verifiable fair-value estimates. At the firm level, leverage is a noisy measure of assets-in-place because it also proxies for distress (especially likely in our sample where all firms have BTM > 1). Industry mean
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In unreported tests, we find the VNA ratio has high in-sample variance; we also find relatively high kurtosis in this variable, suggesting the high variance is due to a few extreme observations. To mitigate the effect of such observations (and to avoid trimming-induced data loss in what is a relatively small dataset), we use in-sample ranks in computing UNA.
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leverage, however, can average out the firm-specific distress component of leverage, leaving us with a proxy for assets-in-place. The higher the industry’s average leverage, the more likely the nature of assets in a firm are such that they can be reliably valued; and thus, the less likely the unverifiability of net assets. We define ―industry‖ as four-digit NAICS codes, and rank all such industries by mean leverage. We use the industry mean leverage rank, IndLev, as a proxy for verifiability of firm assets. Thus, we expect the subjectivity generated by unverifiable net assets to decrease in IndLev.
In addition to the two financial characteristics described above (i.e., the number and size of reporting units and the unverifiable net assets in reporting units), Ramanna (2008) describes a third firm attribute that can facilitate write-off management: the fair-value-to-book-value ratio (FTB) of reporting units. Under Step 1 of SFAS 142, impairment losses are recognized only when the unit’s estimated fair value is less than the book value of its net assets. This step implicitly assigns all of the difference between a unit’s fair value and its book value to acquired goodwill. However, at least two other factors can be responsible for this difference. First, internally generated growth options can increase the unit’s fair value without increasing its book value. Second, the book value of the unit’s recorded net assets can be below their market value. These two factors mean that the value implicitly allotted to acquired goodwill in Step 1 of the SFAS 142 impairment test can be overstated. Units with high FTB ratios can avoid impairment of acquired goodwill even when that goodwill is impaired because internally generated rents and understatement of net assets can absorb any drop in goodwill value. We do not explicitly test this hypothesis. We cannot directly observe FTB ratios, and the best available proxy for FTB ratios—firm-wide market-to-book ratios—is used to select our sample. In other words, since our sample only consists of firms with BTM > 1, testing whether firms with high FTB are avoiding impairment losses is not possible. This suggests that the extent of non-impairment documented in this paper is a lower bound on the total avoidance occurring under SFAS 142.
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To examine the determinants of goodwill non-impairment in the sample in a multivariate setting, we estimate the following regression for all firm-years ―i.‖
Impi = Intercept + βk*(Private information motivesi) + βl*(Contracting motivesi) +βm*(Reputation motivesi) + βn*(Valuation motivesi) + βo*(Reporting flexibilityi) + βp*(Control variablesi) + εi … (1)
In the above equation, Impi measures a firm’s goodwill impairment at the end of year t (i.e., the second full fiscal-year with BTM>1), scaled by beginning-of-period assets. If impairments are not reported on COMPUSTAT (i.e., if impairments are coded as ―missing‖ or ―combined‖ with other data), we assume impairments are zero.17
The set of ―private information motives‖ is either InfoAsym alone or (in a separate regression) InfoAsym together with the content-analysis based variables, Achievement and Fog. The content-analysis based variables are excluded from the first regression specification because they cannot be computed for six firms in the sample due to limitations in obtaining these firms’ 10-K filings.18
The set of ―contracting motives‖ is CovDebt, Bonus, and Delist; ―reputation motives‖ is Tenure; and ―valuation motives‖ is AsstPrc. AsstPrc cannot be estimated for 38 of the 124 sample firms due to data restrictions and so is excluded from the primary regression specification. The set of ―reporting flexibility‖ proxies is each of four combinations (estimated across four separate regressions) of a goodwill allocation variable (i.e., ln(Seg) or HHI) and an unverifiability variable (i.e., UNA or IndLev). The control variables from equation (1) are as follows.
(a) Size: The natural log of beginning-of-period assets.
(b) PropGw: The ratio of beginning-of-period goodwill to prior year assets. (c) BHRet: The ―year t‖ buy-and-hold return.
(d) NumQtrBTM>1: The number of quarters in ―year t‖ with BTM > 1.
Size and PropGw are likely proxies for the magnitude of goodwill write-offs, while BHRet and NumQtrBTM>1 are likely proxies for the economic necessity of a
17 We tested this assumption for a random sample of ten firms by searching 10-K filings for impairments data. We found no instance where our assumption was inconsistent with data in the 10-K.
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write-off. Unless otherwise mentioned, all variables in our analyses are measured at the end of ―year t.‖ See Appendix A for a consolidated description of all variables. Parameter estimates in the multivariate regression are computed using ordinary least squares and standard errors are adjusted for heteroskedasticity as suggested by White (1980).
3. Results
3.1. Sample descriptive statistics
Table 1 reports summary statistics for all variables in our analyses. The median impairment (Imp) in our sample is zero, while the mean is 4.7% of beginning-of-period assets. About 24% of sample firms engage in net share repurchase activities in year t, while about 21% engage in net insider buying. The mean value of InfoAsym is 0.4113 (i.e., about 41% of the sample engage in either repurchase or insider buying activities). Mean Achievement, the proportion of sample firms with more ―achievement‖ words than their matches, is 0.5593. Mean Fog, the proportion of the sample with unreadable 10-Ks, is 0.7203.19
Turning to the agency-theory based proxies, the median firm’s CovDebt (i.e., the product of firm leverage and an indicator if the firm has an outstanding net worth or net income based debt covenant) is 0.1341. Nearly 52% of CEOs of sample firms received a bonus in year t and the median (mean) CEO was in office for 4 years (5.5 years) by the end of year t. Over 51% of sample firms have delisting-based incentives to avoid impairments.
The median value of ln(Seg) is 0.693, suggesting that the median firm in our sample has two business segments. The median HHI in the sample 0.768, suggesting that most firms in the sample are not concentrated in one segment. The median industry-leverage rank for firms in the sample is 1192 (the highest possible rank in COMPUSTAT is 2133). The median value of UNA is 63 by construction.
The median (mean) firm in our sample has $210 million ($273 million) in assets and 14.1% (25.6%) of its assets in goodwill. Sample firms experience a median (mean) year-t buy-and-hold return of -7.2% (5.1%). The median firm closed each of its four
19 Li (2008, p. 227) reports that the 25th percentile value of the Fog Index in a broad sample of firms on COMPUSTAT is 18.44, suggesting the proportion of COMPUSTAT firms with unreadable 10-Ks is over 75%.
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quarters in year t with BTM > 1, while on average, firms in the sample experienced 3.49 quarters of BTM > 1 in year t.
3.2. Association of sample non-impairment with private information
Table 2 reports the results of univariate frequency tests of association between goodwill non-impairment and the proxies for managers’ private information on positive future cash flows. As noted earlier, there are 124 firms in COMPUSTAT meeting our sample criteria. Of these, 31% (38 firms) record goodwill impairments in year t, the second full fiscal-year with BTM>1; 69% (86 firms) do not. It is possible that managers avoid goodwill write-offs due to private information on positive future cash flows. To test this explanation, we examine whether non-impairment firms are more likely to show evidence of net repurchase or insider buying activity (i.e., InfoAsym=1). Panel A of Table 2 reports that the frequency of firms with InfoAsym=1 among non-impairers (42%) is statistically indistinguishable from that among impairers (39%). The chi-square statistic for the 2x2 comparison has a p-value of 0.8034.
In Panel B of Table 2, we report on the association of non-impairment with InfoAsym among sample firms with only one business segment. Earlier, we argued that firms with large numbers of, and large-sized, reporting units are more likely to be able to avoid impairments by strategically allocating goodwill to units with internally generated rents. If this is the case, it is useful to determine if, in single-segment firms, unverifiability in net assets provides adequate discretion to avoid write-offs. Panel B reports the year-t frequency of non-impairment among single-segment sample firms is 69% (36 of 52 firms), the same as the frequency of non-impairment among all sample firms. Further, among single-segment firms, the frequency of firms with InfoAsym=1 among non-impairers (39%) is statistically indistinguishable from that among impairers (31%); p-value is 0.5975. Thus, non-impairment does not appear to be limited to multi-segment firms, nor does the evidence confirm that non-impairment in single-multi-segment firms is motivated by managers’ private information on future cash flows.
Positive net insider buying is arguably a stronger measure of favorable information asymmetry (between managers and shareholders) than InfoAsym (recall, InfoAsym is firms with either positive net share repurchase activity or positive net insider
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buying). We thus test whether goodwill non-impairment frequency is associated with favorable information asymmetry when the latter is defined only by positive net share repurchase (Repurchase) or by positive net insider buying (Inside). The results are reported in Panels C and D of Table 2. We find: (1) the frequency of firms with Repurchase=1 among non-impairers (24%) is statistically indistinguishable from that among impairers (24%); (2) the frequency of firms with Inside=1 among non-impairers (22%) is statistically indistinguishable from that among impairers (18%). The p-values for the comparisons are 0.9298 and 0.6430, respectively. Overall, the data cannot confirm that non-impairment in the sample is due to management’s possession of favorable private information, at least as based on share repurchase and insider buying activity.
We next turn to tests of the private information hypothesis that are based on content-analyses of firms’ 10-Ks. In these tests, six of the 124 sample firms are excluded because they failed to file 10-Ks within eight months of their fiscal year-ends. In Panel E of Table 2, we find that the proportion of firms with high Achievement (i.e., Achievement=1) among non-impairers in the sample (57%) is statistically indistinguishable from that among sample impairers (54%); the p-value is 0.7812. Thus, positive private information as manifested by an incidence of Achievement is unable to explain non-impairment in the sample. In Panel F of Table 2, we report on the association of goodwill non-impairment with Fog. If positive private information motivates non-impairment, we expect a lower prevalence of Fog=1 among sample non-impairers than among sample impairers. However, we find the proportion of non-impairing sample firms with Fog=1 (78%) is statistically higher than that of impairing sample firms (59%). The p-value on this comparison is 0.0397. Thus, while we find an association between non-impairment and Fog, the relation is in the opposite direction as that predicted by the private information hypothesis.
The tests of the private information hypothesis reported on thus far are subject to certain caveats, as discussed in Section 2. To mitigate concerns generated by these caveats, we also investigate whether sample non-impairers have, on average, higher one-year-ahead stock returns than sample impairers. If this is the case, then non-impairment is consistent with managers having positive private information that is revealed publicly,
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and incorporated into stock prices, over the following one year. The results of tests based on one-year-ahead stock returns are presented in Table 3.
For the 124 firms in our sample, stock returns data over the following 12 months continue to be available on CRSP for 96 firms. These firms are classified as ―Active, CRSP‖ in Table 3. Sixty-four of these 96 firms are identified as non-impairers in our sample year; the other 32 are impairers. As the data in the table show, the mean one-year-ahead return for ―Active, CRSP‖ firms not taking goodwill impairment (n=64) is higher than that for such firms who take goodwill impairment (n=32). However, the opposite is true when comparing the medians across the two groups. Neither comparison is statistically significant. Thus, we cannot reject the null that there is no difference in one-year-ahead returns between goodwill impairers and non-impairers. These data are inconsistent with the proposition that goodwill non-impairment in the sample year is due to managers’ private information, information that subsequently (over one year) becomes public and is incorporated into stock prices.
For the remaining 28 sample firms without one-year-ahead returns data on CRSP (i.e., 124 minus 96), we attempt to gather the returns data from Thomson One Banker. Fifteen of the 28 firms have 12-month-ahead stock price information available on Thomson. From this price information, we compute the 12-month-ahead stock returns; these firms are classified as ―Active, not CRSP‖ in Table 3. We are not certain why these firms, previously covered by CRSP, are dropped in the year ahead. Irregular price reporting and low-volume trades, as reported in Thomson, suggest that these firms are thinly traded during the year ahead. Twelve of the 15 firms are identified as non-impairers in our sample year; the other three are non-impairers. Among ―Active, not CRSP‖ firms, the mean and median one-year-ahead returns for firms not taking goodwill impairment (n=12) are higher than those for firms taking goodwill impairment (n=3). However, the comparisons are not statistically significant, and moreover, the number of observations among impairers in this group (n=3) is too small to facilitate reliable statistical inferences.
A further six of the 28 firms without one-year-ahead returns data on CRSP have stock price data available on Thomson for some period less than the following 12 months: these firms are either delisted or acquired over the 12 month period. For these
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firms, returns are calculated from the sample fiscal-year-end date to the date of the last reported stock trade. Since these firms do not trade for a full 12 months after the sample fiscal-year-end, they are referred to as ―Inactive‖ in the table below. Five of the ―Inactive‖ firms are identified as non-impairers in our sample year; the other one firm impaired in the sample year.
Finally, seven firms of our sample of 124 have no year-ahead stock returns data on either Thomson One Banker or CRSP. Of these seven, we identify four as being acquired over the year following the sample year, and another one as being delisted. No further data on the other two are available. Five of the seven firms are identified as non-impairers in our sample year; the other two are non-impairers. A more detailed analysis of the acquisition and delisting status of sample firms in the one-year ahead is provided in Table 7, which is discussed at the end of this section.
3.3. Association of sample non-impairment with agency-theory predictions
In Table 4, we report on univariate frequency tests of association between goodwill non-impairment and the three contracting-based motives described in §2.3.2, specifically, CovDebt, Bonus, and Delist. Since CovDebt is not a binary variable, we define an indicator DCovDebt for use in the frequency tests; DCovDebt is set to one if the firm has an outstanding net-income or net-worth based covenant.
Panel A of Table 4 reports a statistically higher proportion of firms with DCovDebt=1 among sample non-impairers (78%) than among sample impairers (63%). The p-value for this comparison is 0.0867. The proportion of non-impairing sample firms whose CEOs are likely to have goodwill-inclusive compensation contracts (Bonus=1) is reported in Panel B of Table 4. This proportion (57%) is statistically higher than that of impairing sample firms (39%); the p-value is 0.0722. The association of goodwill non-impairment and Delist is tested in Panel C of Table 4. We find that the proportion of firms with Delist=1 among non-impairers in the sample (51%) is statistically indistinguishable from that among sample impairers (53%); the p-value is 0.8801. Thus, overall, there is evidence that goodwill non-impairment in the sample is associated with debt and CEO compensation contracting concerns; while we cannot confirm that non-impairment is associated with delisting-based concerns.